{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('../resnet50_finetuning.log','r')\n",
    "text = f.read().split('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = []\n",
    "for line in text:\n",
    "   res.append(line.split('\\t'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = res[:len(res)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y0 = []\n",
    "y1 = []\n",
    "for line in res:\n",
    "    y0.append(float(line[1])/10)\n",
    "    y1.append(float(line[2])/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7faf1c9fd400>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(0,len(res))\n",
    "plt.plot(x,y0)\n",
    "plt.plot(x,y1)\n",
    "plt.legend(['train loss','train accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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